Multi-maneuvers algorithms for multi-risk collision avoidance via nonconvex quadratic optimization - LAAS - Laboratoire d'Analyse et d'Architecture des Systèmes
Communication Dans Un Congrès Année : 2023

Multi-maneuvers algorithms for multi-risk collision avoidance via nonconvex quadratic optimization

Résumé

In this article, the problem of modeling and computing the optimal collision avoidance maneuvers for multiple short-term encounters is presented. Several metrics defining the overall collision risk are analyzed. These include imposing lower bounds on the miss-distance or the Mahalanobis distance at each corresponding time of closest approach (TCA) or upper-bounds on the orbital collision probability. The avoidance maneuvers are modeled as impulsive ones in a single direction of the local frame and for a priori fixed dates for operational motivations. Station-keeping constraints are also imposed via linear inequalities on the relative states at each TCA. The specific nature of the imposed constraints is dictated by a practical framework provided by the French Space Agency (CNES). This results in formulating the maneuver design problem as an optimization problem with linear objective and nonconvex quadratic constraints. Different algorithms are presented to solve this problem. Finally, the relative efficiency of the proposed approaches is evaluated and analyzed on two realistic conjunctions built from data extracted from the CNES database and on academic examples of higher complexity.
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Dates et versions

hal-03847541 , version 1 (10-11-2022)

Identifiants

  • HAL Id : hal-03847541 , version 1

Citer

Matthieu Masson, Denis Arzelier, Mioara Joldes, Bruno Revelin, Jérôme Thomassin. Multi-maneuvers algorithms for multi-risk collision avoidance via nonconvex quadratic optimization. IFAC World Congress 2023, Jul 2023, Yokohama, Japan. ⟨hal-03847541⟩
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